Method for determining a spatial quality index of regionalised data

ABSTRACT

The method of the invention includes the following stages:  
     a first stage for identifying the first order statistical anomalies on the basis of a set of raw regionalised data,  
     a second stage for identifying second order statistical anomalies on the basis of the first order anomalies with extraction of the components considered as anomalistic components and components considered as coherent in space,  
     establishing a quantified relation between any combination of the estimated values of the anomalistic components of the first and/or second order and any combination of the estimated values of the coherent components of the first and/or second order.  
     The method can be applied in particular to geophysical data, image data obtained by physical methods or even to any type of sampling of natural phenomena.

BACKGROUNG OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention concerns a method for determining a spatialquality index of regionalised data.

[0003] It can be applied more particularly, but not exclusively, togeophysical data, image data obtained by physical methods, such asmedical or sonar methods, the non-destructive control of materials, oreven to any type of sampling of natural phenomena, such as minesrecognition campaigns, geochemical inventories, pollution sensors,satellite data, oceanographic data, water analysis, etc.

[0004] 2. Description of the Prior Art

[0005] The regionalised data is data marked by coordinates inside aspace with N dimensions, that is most currently in a one, two or threedimensional geographical space. This data can be either mono ormultivariable, that is that one or several variables are measured orcalculated at the data points.

[0006] The theory of regionalised variables is known under the name ofgeostatistics. Geostatistics involves applying probabilities to naturalphenomena which develop in space over a period of time, hence the prefix‘geo’. This theory is shown in detail in the work entitled “The theoryof regionalised variables” by G. MATHERON (Publisher MASSON).

[0007] This theory provides the appropriate language and tools forestimating any quantity, a priori unknown, and able to be marked in agiven space on the basis of a forcefully fragmentary sampling of thissame quantity.

[0008] So as to estimate this unknown quantity, geostatistics makes itpossible to suitably select the most appropriate probabilistic model forthe situation, the geostatistical estimator being known under the nameof “krigeage”.

[0009] More than the estimation, the probabilistic model also gives anindicator of the accuracy of the estimate. This indicator, known asestimate variance, is a vital tool as it opens the way for a possiblecontrol of uncertainties (translated in variance terms).

[0010] Within the context of stationary probabilistic models, whichassumes the invariance by translating in space the average of themodelised variable, the covariance tool or variogramme is used toquantify the spatial variability of the data.

[0011] For a non-stationary model, generalised covariance is used.

[0012] The geostatistical models also make it possible to validlyanticipate concerning a future state, for example the exploitation ofnatural resources, when the data available shall be more numerous andthe operator needs to deal with estimating problems.

[0013] Irrespective of the context for exploiting natural resources, thequestion here still concerns whether there is sufficient data availableto resolve the operational problem.

[0014] Added to the intrinsic quality of each data item is the qualityof the spatial integration of this data element inside the whole set ofdata. This is why it is advantageous to complete the experimentalreading by a geostatistical control associated with geographical, timeor other types of coordinates.

[0015] The usual methods for controlling the quality or coherence ofsets of regionalised data are either visual or morphological (studies ofshapes) or statistical (without taking into account the spatialcoordinates). When they are used, filtering methods (frequency orspatial) generally work on monovariable data and on regular grids. As aresult, they are ill-adapted to the breaking up of multivariable datairregularly situated in space into anomalistic and coherent components;

[0016] Similarly, the definition of the criteria used to define theanomalies is often arbitrary and ill-adapted to experimentalverification.

OBJECT OF THE INVENTION

[0017] So as to eliminate these drawbacks, the invention proposesquantifying the spatial quality of a set of regionalised data by virtueof determining a geostatistical index known as a “Spatial Quality Index”(SQI) being used to localise a priori anomalistic data and thus judgingthe quality of the measurements or of the digital processing which havegenerated the set of data.

[0018] The determination of the SQI resolves both the problem ofinterpretation of the spatial variations of the mono or multivariabledata in general terms of anomalies and coherent component and anestimate of the degree of anomalistic or spatial incoherence present ineach data element taken individually. The determination of the SQI doesnot assume any particular arrangement of the data in space and alsofully works on data irregularly distributed in space and also on dataregularly situated at the nodes of a grid with N dimensions, for examplea three-dimensional acquisition grid for acquiring irregularlydistributed geophysical data is defined along two data acquisitiontransversal/longitudinal axes and a third time vertical axis.

SUMMARY OF THE INVENTION

[0019] Advantageously, this index is determined by means of the methodof the invention which includes the following operational stages:

[0020] a first phase for identifying the statistical anomalies of afirst order on the basis of a set of raw regionalised data, thisidentification including a stationing of the data via a preliminaryextraction of the spatial drifts of said data and the determination ofthe associated stationary residue of first order so that the value ofthe average of the residual data is reasonably constant in space, theanomalies being identified and examined on the first order residue so asto provide a first order anomalistic criterion,

[0021] a second phase for identifying a second order statisticalanomalies with extraction of the components of first order residueconsidered as anomalies and the components of first order residueconsidered as coherent in space,

[0022] the establishment of a quantified relation (SQI) between anycombination of the estimated values of the anomalistic components of thefirst and/or second order and any combination of the estimated values ofthe coherent components of the first and/or second order,

[0023] the localisation of space anomalies on the basis of the values ofthe SQI of each regionalised data element.

[0024] Advantageously, said drawing up of a quantified relationconstitutes the determination for each regionalised data element takenindividually of the ratio of a spatial quality index (SQI).

[0025] Of course, said identification stages could be carried out by ageostatistical estimation (krigeage) in a non-stationary model for thefirst phase and in a stationary model for the second phase.

[0026] In the first phase, the non-stationary estimation of the spatialdrift makes it possible to obtain first order stationary residue onwhich it is possible to validly calculate and modelise a variogramme.

[0027] The interpretation of this variogramme in terms of coherent andanomalistic components results in the estimation per stationary model ofthe second order anomalistic component.

[0028] More particularly, the stationary and non-stationarygeostatistical models could use:

[0029] the estimation by factorial krigeage of the anomalistic andcoherent components of the residue,

[0030] the definition of the krigeage surrounding area adapted to theestimation of each of said anomalistic and coherent components.

[0031] Of course, in each of said stages, the analysis could befacilitated by a 3D visual control carried out firstly by aninterpolation on a “grid” file of any irregularly sampled variableoriginating from a “point” file, and secondly with by means of a colourcode associated with the value of the inserted variable.

BRIEF DESCRIPTION OF THE DRAWINGS

[0032] One embodiment of the invention appears hereafter and is given byway of non-restrictive example with reference to the accompanyingdrawings on which:

[0033]FIG. 1 is a diagrammatic representation illustrating the mainphases for determining the spatial quality index (coefficient of spatialanomalies) inside a geophysical data processing context;

[0034]FIGS. 2a, 2 b and 2 c represent various file formats used in themethod of the invention;

[0035]FIGS. 3a and 3 b respectively represent a target diagram (FIG. 3a)and a speed/time diagram (FIG. 3b);

[0036]FIG. 4 represents displays allowing a visual control of the stagesof the method;

[0037]FIG. 5 represents the interpolation made to allow the displays ofFIG. 4;

[0038]FIG. 6 represents an experimental residue variogram;

[0039]FIGS. 7a and 7 b represent spatial anomalistic quantification andlocalisation modes.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0040] This example more particularly concerns the acquisition andprocessing of 3D seismic data for characterising petrol tanks and more.particularly the quality control of measurements of geophysical speedsor “stack” speeds.

[0041] The problem is as follows: the seismic contractor offers thepetrol operator a set of speeds set up manually for the “stack”operation which conditions the quality of the final data. The petroloperator is responsible for monitoring the work of the contractor andshall give his opinion concerning the quality of the speed set-up.

[0042] To this effect, he can examine the cubes of “stack” speedsset-ups with the aid of statistical and geophysical tools so as toidentify the spatial incoherences due to erroneous set-ups.

[0043] The determination of the SQI in accordance with the method of theinvention contributes in defining the set-ups considered as anomalisticset-ups fit for resetting-up so as to guarantee a spatial homogeneitywhich shall be quantified by the SDI index value.

[0044] Each “stack” speed set-up is defined by its spatialcoordinates—geographic and temporal—and a geophysical speed value.Inside the spatial field described by all the set-ups, a probabilisticmodelisation makes it possible to separate a spatial noise, possiblyorganised, of a coherent signal (spatially). The spatial noise issupposed to correspond to processing and acquisition artifacts.Quantified, it makes it possible to quickly identify the problematicset-ups according to a tolerance threshold deduced from geophysicalquality or other (for example in a “stack” case, preservation of theamplitudes of the seismic signal) requirements.

[0045] In the process for determining the SQI index shown on FIG. 1, thesystem of coordinates retained is the seismic longitudinal axis(inline)—transversal axis (crossline)—time system (FIG. 2a). In fact,the entire geophysical chain, from acquisition to seismic processing,favours these three main directions: vertical (time) and the horizontaldirections (inline, crossline) defined by the acquisition device. As aresult, most of the acquisition and processing artifacts are generatedalong these directions and the analysis is orientated along thesedirections. This process thus makes it possible to reduce thedetermination time.

[0046] More specifically, the process introduces three types of files:

[0047] a 3D “point” file (cube of “stack” speed set-ups) (FIG. 2a)defined by all the positions of speed set-ups in a system of coordinateslongitudinal axis (inline)—transversal axis (crossline)—time, isgenerally at regular step in the horizontal plane (longitudinal axis(inline)—transversal axis (crossline)) and irregular in time,

[0048] a 2D “grid” file (FIG. 2b): with regular step and definedaccording to the longitudinal (inline)—transversal (crossline) axes andused to produce directional statistics along the vertical (time axis ofthe 3D point file of FIG. 2a); statistics according to the longitudinaldirection or transversal direction are possible,

[0049] a 3D “grid” file (FIG. 2c): with regular mesh in a system ofcoordinates (longitudinal axis (inline)—transversal axis(crossline)—time) and used for various displays, as explainedsubsequently (FIG. 4).

[0050] In accordance with the methodology shown on FIG. 1, once the cubeof “stack” speed set-ups is loaded, it is subjected to a geostatisticalquality control (calculation of SQI). The raw speeds 1 are broken downinto the first order by factorial “krigeage” into a spatial drift 2(“low frequency” component) and stationary residue 3 (QC1 phase). Thespatial coherence of the first order residue is modalised (with the aidof a variogram) (QC2 phase) for embodying a discriminating filtering viathe factorial “krigeage” between a spatial noise 4, that is a secondorder residue, and a second order coherent portion 5 considered to be“cleaned” from the processing and acquisition artefacts (QC3 phase).This second order coherent residual portion is added to the drift 2,that is the first order coherent portion, so as to generate a cube ofspatially coherent speeds set-ups 6 (QC4). In this example, the SQI isconstituted by the ratio between the second order residue noise and thecoherent portion of the data element, that is the sum of the coherentcomponents of the first and/or second order and result in obtaining aspatial anomalistic cube 7 (QC5 phase): each set-up is thuscharacterised by its spatial quality index (SQI) which expresses thespatial noise percentage with respect to the spatially coherent portion.

[0051] The experimental statistics calculated during the process arebroken down into:

[0052] basic statistics,

[0053] directional statistics,

[0054] experimental variogram.

[0055] All the “stack” speeds set-ups and the first order residues formdistributions which can be quickly analysed by tools taking variousparameters into account, such as its number of samples, its extremepoints, its arithmetic mean, its standard deviation, its variance:

[0056] its number of samples N which characterises a distribution V₁(raw speeds, residues, drifts, anomalies, filtered residues, filteredspeeds . . . )

[0057] its extreme points:

minimum=min (V_(i))

maximum=max (V_(i))

[0058] its arithmetical mean:$m = {\frac{1}{N}{\sum\limits_{i = 1}^{N}V_{i}}}$

[0059] its standard deviation:$\sigma = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {V_{i} - m} \right)^{2}}}$

[0060] its variance:$\sigma^{2} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {V_{i} - m} \right)^{2}}}$

[0061] This analysis can be completed by using:

[0062] a target diagram in which the values V_(i) of the variable aregrouped into categories, the target diagram representing frequenciescorresponding to these categories (FIG. 3a),

[0063] a time/speed diagram (“cross-plot”) (FIG. 3b)

[0064] It is known that the acquisition and processing artefacts have asmain axes the longitudinal, transversal and time axes. The calculationof a statistical magnitude along one of these three directions canresult in identifying one or several reinforced artefacts. As a result,the three statistical magnitudes calculated along these directions couldbe limited to the number of samples, the arithmetical mean ands thevariance (or standard deviation).

[0065] Added to the previously mentioned analysis tools is the firstorder residue experimental variogram. The variogram is able to quantifythe spatial correlation of a regionalised variable V({right arrow over(r)}), r being the position vector defined in thelongitudinal-transversal-time system of coordinates. Its formula isdeduced from that of the theoretical variogram which concerns the randomfunction V({right arrow over (r)}) for which there is only oneembodiment: the regionalised variable.

[0066] Theoretical variogram:${\gamma \left( \overset{->}{h} \right)} = {\frac{1}{2}{{Var}\left\lbrack {{V\left( {\overset{->}{r} + \overset{->}{h}} \right)} - {V\left( \overset{->}{r} \right)}} \right\rbrack}}$

[0067] where {right arrow over (h)} is the vector characterising a setof pairs of set-ups

[0068] Experimental variogram (after stationary and ergodicityhypotheses):${\Gamma \left( \overset{->}{h} \right)} = {\frac{1}{2{N\left( \overset{->}{h} \right)}}{\sum\limits_{n = 1}^{N{(\overset{->}{h})}}\left\lbrack {{V\left( {{\overset{->}{r}}_{n} + \overset{->}{h}} \right)} - {V\left( {\overset{->}{r}}_{n} \right)}} \right\rbrack^{2}}}$

[0069] where N({right arrow over (h)}) is the number of pairs of set-upsseparated from {right arrow over (h)}.

[0070] Moreover, the analysis could be facilitated by using the displaymode shown on FIG. 4.

[0071] According to this display mode, interpolation on grid (withregular steps) of any irregularly sampled variable (“point” file)allows, by means of a colour code associated with the value of theinterpolated variable, a quick 3D visual control.

[0072] The interpolation retained by display is defined as follows (FIG.5): at one grid node P_(j), the estimated value corresponds to thelinear interpolation of the two set-ups P₁, P₂ respectively defined bythe nearest coordinates (t₁, V₁) and (t₂, V₂) situated on both sides ofthe node P_(j) and on the same vertical line as the latter.$V_{j} = {V_{1} + {\left( \frac{t_{j}}{t_{2} - t_{1}} \right) \times \left( {V_{2} - V_{1}} \right)}}$

[0073] Other more elaborate types of interpolation, such as “krigeage,”could be possible for display if required.

[0074] The stages for filtering and determining the anomalies used inthe methodology shown on FIG. 1 are described hereafter:

[0075] a) Drift/residue filtering stage

[0076] A field of speeds generally has a vertical drift due tocompaction, this compaction being the increase of the speed according tothe increase of the penetration depth (a horizontal drift may alsoexist, if the sea bottom, for example, is considered for acquiringmarine seismic data). A non-stationary state of speeds is observed inthis direction which can be managed by the theory of generalisedcovariances, a non-stationary geophysical model. But a generalisedcovariance cannot be interpreted directly in terms of anomalistic andcoherent spatial components, thus rendering it impossible to adjust amodel. Therefore, it is necessary to extract this drift and work on theassociated stationary residue.

[0077] The extraction of a drift ensuring the stationary state of theresidue is embodied by least error squares polynomial adjustment whichis a particular case of factorial krigeage: the value of the drift at apoint in space corresponds to the value of a polynomial adjusting asbest as possible (least error squares) the points (independent)belonging to a surrounding region centered around the point to beestimated. The type of the polynomial—1 z, 1 z z², 1 x z x² z² xz,etc—is to be determined according to the type of drift it is desired toextract, z being the time and x being a geographical coordinate. Thedimensions of the krigeage extraction surrounding region shall guaranteethe stationary state of the first order residue.

[0078] Example: extraction of a type 1 z z² drift at the point {rightarrow over (r)}₀:

V _(drift)({right arrow over (r)} ₀)=a+b×z ₀ +c×z ₀ ² with {right arrowover (r)}₀(x₀,y₀, z₀)

[0079] The coefficients of the polynomial (a, b and c) are obtained byminimising the system:$\sum\limits_{i = 1}^{N_{V}}\left\lbrack {\left( {a + {b \times z_{i}} + {c \times z_{i}^{2}}} \right) - {V\left( {\overset{->}{r}}_{i} \right)}} \right\rbrack^{2}$

[0080] where N_(V) is the number of samples contained in the surroundingregion centered around the point to be estimated.

[0081] b) Coherent residue/anomalies filtering

[0082] The drift, a first order coherent component, previouslyestimated, is subtracted from the raw “stack” speeds. The first orderresidues shall be stationary.

V _(residue)({right arrow over (r)} ₀)=V({right arrow over (r)} ₀)−V_(drift)({right arrow over (r)} ₀)

[0083] The variographic analysis of the first order residues is thecrucial phase of spatial quality control.

[0084] The experimental variogram of the residues Γ({right arrow over(h)}) is calculated in several directions. For reasons of providingclarity of the figures, only the components in the three main directions(longitudinal, transversal, time) are shown on FIG. 6.

[0085] The modelisation of the variogram is subordinate to aninterpretation of the experimental variogram in terms of coherent andanomalistic spatial components. The combined skills of thegeostatistician, the geohphysicist and possibly the geologist arerequired for this interpretation phase. The geologist providesinformation concerning the known or assumed geological structures, thegeophysicist specifies the nature of the major geophysical artefactslikely to affect the data, and the geostatistician constructs thevariogram model by taking into account these two types of information.

[0086] Ideally, it would be better to separate a spatial noise(anomalistic spatial component) from a coherent signal (coherent spatialcomponent) solely on the basis of a variographic interpretation. Themodelisation put forward depends on the application for producing a setof speeds (“stack”, depth conversion, DIX speeds . . . ). A spatialcomponent can be considered as a noise for a certain application or as acoherent signal for another. The terms “coherent” and “anomalistic” arenot intrinsic properties of the set of speeds but are the properties ofthe set of speeds within the context of the recognised geostatisticalmodel.

[0087] The adjustment of the model Γ^(M)({right arrow over (h)}) canonly be effected by conditional negative standard functions. Initially,the variogram models, like the nugget effect model and the exponentialand spherical models, are sufficient to construct an extendable model(with several components). The definition of the models is given in theisotropic case:

[0088] Nugget effect model:${\Gamma \left( \overset{->}{h} \right)} = \left\{ \begin{matrix}0 & {{{si}\quad {\overset{->}{h}}} = 0} \\b & {{{si}\quad {\overset{->}{h}}} > 0}\end{matrix} \right.$

[0089] Spherical model:${\Gamma \left( \overset{->}{h} \right)} = \left\{ \begin{matrix}0 & {{{si}\quad {\overset{->}{h}}} > a} \\{b \times \left\lbrack {{\frac{3}{2} \times \frac{\overset{->}{h}}{a}} - {\frac{1}{2} \times \left( \frac{\overset{->}{h}}{a} \right)^{3}}} \right\rbrack} & {{{si}\quad 0} \leq {\overset{->}{h}} \leq a}\end{matrix} \right.$

[0090] Exponential model:${\Gamma \left( \overset{->}{h} \right)} = {b \times \left\lbrack {1 - {\exp \left( {- \frac{\overset{->}{h}}{a}} \right)}} \right\rbrack}$

[0091] The parameters a and b respectively are termed the range andstage of the variogram and are both positive.

[0092] The retained variogram model Γ^(M) is a linear combination ofvarious elementary components selected according to their coherent oranomalistic interpretation:

Γ^(M)({right arrow over (h)})=Γ_(A)({right arrow over (h)})+Γ_(C)({rightarrow over (h)})

[0093] with Γ_(A) a component of the variogram associated with theanomalistic portion,

[0094] Γ_(C) a component of the variogram associated with the coherentportion.

[0095] Surrounding Area

[0096] The surrounding area combines all the points taking part inestimating the anomalistic component situated at the point {right arrowover (r)}₀.

[0097] A sliding surrounding area is essential for any filteringoperation. A single surrounding area including all the samples of thefield is extremely penalising concerning the calculation time. Thedimensions of the sliding surrounding area thus need to optimise thecalculation time without deteriorating the quality of the estimate.

[0098] Factorial Krigeage

[0099] The modelisation of the second order variogram of the residuecorresponds to an interpretation in terms of anomalistic and coherentcomponents. The factorial krigeage allows an estimate of each of the twocomponents.

[0100] The estimate of the anomalistic component at the point {rightarrow over (r)}₀ is carried out by calculating:${V_{{anomalistic}\quad {residue}}\left( {\overset{->}{r}}_{0} \right)} = {\sum\limits_{\alpha = 1}^{N_{V}}{\lambda_{\alpha} \times {V_{residue}\left( {\overset{->}{r}}_{\alpha} \right)}}}$

[0101] where all the {right arrow over (r)}_(α) a constitute the slidingkrigeage surrounding area and where the krigeage weights are determinedby resolving the system: $\left\{ {{{\begin{matrix}{{{\sum\limits_{\beta = 1}^{N_{V}}{\lambda_{\beta} \times {\Gamma^{M}\left( {{\overset{->}{r}}_{\alpha} - {\overset{->}{r}}_{\beta}} \right)}}} + \mu_{A}} = {\Gamma_{A}\left( {{\overset{->}{r}}_{\alpha} - {\overset{->}{r}}_{0}} \right)}} \\{{\sum\limits_{\beta = 1}^{N_{V}}\lambda_{\beta}} = 1}\end{matrix}\quad {for}\quad \alpha} = 1},\ldots \quad,N_{V}} \right.$

[0102] The estimation of the coherent component at the point {rightarrow over (r)}₀ can be obtained similarly by factorial krigeage. So asto find the corresponding krigeage weights, it suffices to change Γ_(A)by Γ_(C) in the krigeage system.

[0103] However, a single filtering is required since by means of thefactorial krigeage, the following can be written:

V _(coherentresidue)({right arrow over (r)} ₀)=V _(residue)({right arrowover (r)} ₀)−V _(anomalistic residue)({right arrow over (r)} ₀)

[0104] c) Quantification of spatial anomalies

[0105] Calculation of the Spatial Anomaly Coefficient

[0106] The spatially coherent portion of the second order residue addedto the drift, a first order coherent component, makes it possible togenerate a spatially coherent field of speeds:

V _(coherent)({right arrow over (r)} ₀)=V _(drift)({right arrow over(r)} ₀)+V _(coherent residue)({right arrow over (r)} ₀)

[0107] The ratio between an estimation of the anomalistic component andan estimation of the coherent component of the data element (expressedin %) constitutes a ratio known as a spatial anomaly coefficient orspatial quality index SQI. It is attached to each speed set-up.

[0108] SQI attached to the stack speed set-ups:${V_{{spatial}\quad {anomaly}\quad {coefficient}}\left( {\overset{->}{r}}_{0} \right)} = {\frac{V_{anomalisticresidue}\left( {\overset{->}{r}}_{0} \right)}{V_{coherent}\left( {\overset{->}{r}}_{0} \right)} \times 100}$

[0109] d) Localisation of the anomalistic points via interpretation ofthe spatial anomaly coefficient

[0110] The localisation of the spatial anomalies is made on the basis ofthe spatial anomaly coefficient attached to each set-up of the speedcube. Two options are possible:

[0111] in the first case, two categories of colours (or symbols) areassociated with the spatial anomaly coefficient SQI. Sections in thecube are displayed. It is also possible to interpolate on the grid thespatial anomaly coefficient (FIG. 7a).

[0112] in the second case, the colour (or symbolic) coding relates tothe definition of time intervals possibly containing spatial anomalycoefficients greater than a threshold value (FIG. 7b).

1. Method for determining a spatial quality index of regionalised dataand intended for determining a priori anomalistic data and accordinglyassess the quality of the measurements or of the digital processingwhich have generated said data, comprising the following operationalphases: a first phase for identifying the first order statisticalanomalies from a set of raw regionalised data, this identificationincluding a rendering said data stationary by a preliminary extractionof the spatial drifts of said data and the determination of theassociated first order stationary residue so that the value of theaverage of the residual data is reasonably constant in space, theanomalies being identified and examined on the first order residue so asto provide a first order anomaly criterion, a second phase foridentifying the second order statistical anomalies with extraction ofthe components of first order residue regarded as anomalistic andcomponents of first order residue regarded as coherent in space, settingup a quantified relation between any combination of the estimated valuesof the anomalistic components of the first and/or second order and anycombination of the estimated values of the coherent components of thefirst and/or second order.
 2. Method according to claim 1, wherein thesetting up of the quantified relation is embodied for each regionaliseddata element taken individually.
 3. Method according to claim 1, whereinsaid quantified relation is a ratio.
 4. Method according to claim 1,wherein said quantified relation is the ratio between the estimatedvalue of the second order anomalistic component and the estimated valueof the coherent component is the sum of the first and second ordercoherent components.
 5. Method according to claim 1, wherein said itincludes the localisation of the spatial anomalies on the basis of thevalues of the SQI of each regionalised data element.
 6. Method accordingto claim 1, wherein said identification phases are carried out bygeostatistical estimation in a non-stationary model for the first phaseand in a stationary model for the second phase.
 7. Method according toclaim 1, wherein obtaining first order stationary residue, on which itis possible to calculate and modelise a variogram, is carried out via anon-stationary estimation of the spatial drift.
 8. Method according toclaim 7, wherein the interpretation of said variogram in terms ofanomalistic and coherent components results in the estimation by astationary model of the second order anomalistic component.
 9. Methodaccording to claim 1, wherein said identification phases use: thedefinition of the krigeage surrounding area adapted to the estimation ofeach of said anomalistic and coherent components, the estimation byfactorial krigeage of the anomalistic and coherent components of theresidue.
 10. Method according to claim 1, wherein, in each of saidstages, a 3D visual control of the analysis is carried out by firstly aninterpolation on a “grid” file of any irregularly sampled variableoriginating from a “point” file, and secondly by means of a colour codeassociated with the value of the interpolated variable.
 11. Methodaccording to claim 1, wherein the extraction of a drift ensuring thestationary state of the residue stationary is effected via least errorsquares polynomial adjustment.